The present application relates to software and more specifically to user interface designs and methods for employing visualizations to graphically display and interact with data and/or concepts.
Visualizations are employed in various demanding application, including enterprise resource planning, scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, drug discovery, and so on.
Such applications often involve large datasets, of which a portion or all of the data may be incorporated into a visualization. The data may include patterns and other characteristics that may be illustrated via a visualization. Such applications often demand particularly illustrative visualizations that can reveal patterns and information in the data and facilitate comparisons between data sets.
Illustrative visualizations for visualizing large data sets are particularly important in enterprise applications (e.g., Business Intelligence, Human Capital Management, and so on), where multiple attributes, patterns, and phenomena may exist in granular data to be illustrated via a visualization.
Conventional visualizations, such as pie charts, line graphs, bar graphs, and so on, are typically limited to displaying summarized or aggregated data. More granular data is typically summarized and represented by a portion of a visualization, such as a bar of a bar chart. Unfortunately, patterns potentially existing in underlying granular data are often hidden by the visualizations.
To view underlying data, some visualizations incorporate drill-down functionality, enabling a user to expand or zoom in on a particular portion of a visualization, such as a node, to reveal additional detail. However, performing multiple zooming or drilling operations on individual portions of a visualization may be time consuming and may not provide broader views of granular data between different nodes of a given visualization. Accordingly, important patterns and information among visualization components may remain obscured.